Utilities for Multi-Label Learning

Documentation for package ‘utiml’ version 0.1.7

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-- A --

as.bipartition	Convert a mlresult to a bipartition matrix
as.matrix.mlconfmat	Convert a multi-label Confusion Matrix to matrix
as.matrix.mlresult	Convert a mlresult to matrix
as.mlresult	Convert a matrix prediction in a multi label prediction
as.mlresult.default	Convert a matrix prediction in a multi label prediction
as.mlresult.mlresult	Convert a matrix prediction in a multi label prediction
as.probability	Convert a mlresult to a probability matrix
as.ranking	Convert a mlresult to a ranking matrix

-- B --

baseline	Baseline reference for multilabel classification
br	Binary Relevance for multi-label Classification
brplus	BR+ or BRplus for multi-label Classification

-- C --

cc	Classifier Chains for multi-label Classification
clr	Calibrated Label Ranking (CLR) for multi-label Classification
compute_multilabel_predictions	Compute the multi-label ensemble predictions based on some vote schema
create_holdout_partition	Create a holdout partition based on the specified algorithm
create_kfold_partition	Create the k-folds partition based on the specified algorithm
create_random_subset	Create a random subset of a dataset
create_subset	Create a subset of a dataset
cv	Multi-label cross-validation

-- D --

dbr	Dependent Binary Relevance (DBR) for multi-label Classification

-- E --

ebr	Ensemble of Binary Relevance for multi-label Classification
ecc	Ensemble of Classifier Chains for multi-label Classification
eps	Ensemble of Pruned Set for multi-label Classification
esl	Ensemble of Single Label

-- F --

fill_sparse_mldata	Fill sparse dataset with 0 or " values
fixed_threshold	Apply a fixed threshold in the results
fixed_threshold.default	Apply a fixed threshold in the results
fixed_threshold.mlresult	Apply a fixed threshold in the results
foodtruck	Foodtruck multi-label dataset.

-- H --

homer

Hierarchy Of Multilabel classifiER (HOMER)

-- I --

is.bipartition	Test if a mlresult contains crisp values as default
is.probability	Test if a mlresult contains score values as default

-- L --

lcard_threshold	Threshold based on cardinality
lcard_threshold.default	Threshold based on cardinality
lcard_threshold.mlresult	Threshold based on cardinality
lift	LIFT for multi-label Classification
lp	Label Powerset for multi-label Classification

-- M --

mbr	Meta-BR or 2BR for multi-label Classification
mcut_threshold	Maximum Cut Thresholding (MCut)
mcut_threshold.default	Maximum Cut Thresholding (MCut)
mcut_threshold.mlresult	Maximum Cut Thresholding (MCut)
merge_mlconfmat	Join a list of multi-label confusion matrix
mldata	Fix the mldr dataset to use factors
mlknn	Multi-label KNN (ML-KNN) for multi-label Classification
mlpredict	Prediction transformation problems
mltrain	Build transformation models
multilabel_confusion_matrix	Compute the confusion matrix for a multi-label prediction
multilabel_evaluate	Evaluate multi-label predictions
multilabel_evaluate.mlconfmat	Evaluate multi-label predictions
multilabel_evaluate.mldr	Evaluate multi-label predictions
multilabel_measures	Return the name of all measures
multilabel_prediction	Create a mlresult object

-- N --

normalize_mldata	Normalize numerical attributes
ns	Nested Stacking for multi-label Classification

-- P --

partition_fold	Create the multi-label dataset from folds
pcut_threshold	Proportional Thresholding (PCut)
pcut_threshold.default	Proportional Thresholding (PCut)
pcut_threshold.mlresult	Proportional Thresholding (PCut)
ppt	Pruned Problem Transformation for multi-label Classification
predict.BASELINEmodel	Predict Method for BASELINE
predict.BRmodel	Predict Method for Binary Relevance
predict.BRPmodel	Predict Method for BR+ (brplus)
predict.CCmodel	Predict Method for Classifier Chains
predict.CLRmodel	Predict Method for CLR
predict.DBRmodel	Predict Method for DBR
predict.EBRmodel	Predict Method for Ensemble of Binary Relevance
predict.ECCmodel	Predict Method for Ensemble of Classifier Chains
predict.EPSmodel	Predict Method for Ensemble of Pruned Set Transformation
predict.ESLmodel	Predict Method for Ensemble of Single Label
predict.HOMERmodel	Predict Method for HOMER
predict.LIFTmodel	Predict Method for LIFT
predict.LPmodel	Predict Method for Label Powerset
predict.MBRmodel	Predict Method for Meta-BR/2BR
predict.MLKNNmodel	Predict Method for ML-KNN
predict.NSmodel	Predict Method for Nested Stacking
predict.PPTmodel	Predict Method for Pruned Problem Transformation
predict.PruDentmodel	Predict Method for PruDent
predict.PSmodel	Predict Method for Pruned Set Transformation
predict.RAkELmodel	Predict Method for RAkEL
predict.RDBRmodel	Predict Method for RDBR
predict.RPCmodel	Predict Method for RPC
print.BRmodel	Print BR model
print.BRPmodel	Print BRP model
print.CCmodel	Print CC model
print.CLRmodel	Print CLR model
print.DBRmodel	Print DBR model
print.EBRmodel	Print EBR model
print.ECCmodel	Print ECC model
print.EPSmodel	Print EPS model
print.ESLmodel	Print ESL model
print.kFoldPartition	Print a kFoldPartition object
print.LIFTmodel	Print LIFT model
print.LPmodel	Print LP model
print.majorityModel	Print Majority model
print.MBRmodel	Print MBR model
print.mlconfmat	Print a Multi-label Confusion Matrix
print.MLKNNmodel	Print MLKNN model
print.mlresult	Print the mlresult
print.NSmodel	Print NS model
print.PPTmodel	Print PPT model
print.PruDentmodel	Print PruDent model
print.PSmodel	Print PS model
print.RAkELmodel	Print RAkEL model
print.randomModel	Print Random model
print.RDBRmodel	Print RDBR model
print.RPCmodel	Print RPC model
prudent	PruDent classifier for multi-label Classification
ps	Pruned Set for multi-label Classification

-- R --

rakel	Random k-labelsets for multilabel classification
rcut_threshold	Rank Cut (RCut) threshold method
rcut_threshold.default	Rank Cut (RCut) threshold method
rcut_threshold.mlresult	Rank Cut (RCut) threshold method
rdbr	Recursive Dependent Binary Relevance (RDBR) for multi-label Classification
remove_attributes	Remove attributes from the dataset
remove_labels	Remove labels from the dataset
remove_skewness_labels	Remove unusual or very common labels
remove_unique_attributes	Remove unique attributes
remove_unlabeled_instances	Remove examples without labels
replace_nominal_attributes	Replace nominal attributes Replace the nominal attributes by binary attributes.
rpc	Ranking by Pairwise Comparison (RPC) for multi-label Classification

-- S --

scut_threshold	SCut Score-based method
scut_threshold.default	SCut Score-based method
scut_threshold.mlresult	SCut Score-based method
subset_correction	Subset Correction of a predicted result
summary.mltransformation	Summary method for mltransformation

-- T --

toyml

Toy multi-label dataset.

-- U --

utiml	utiml: Utilities for Multi-Label Learning
utiml_measure_names	Return the name of measures

-- misc --

+.mlconfmat	Join two multi-label confusion matrix
[.mlresult	Filter a Multi-Label Result